Stochastic Programming (Volume 10)

Like its predecessor, this edition presents the theoretical background of generalized linear models (GLMs) before focusing on methods for analyzing particular kinds of data. It covers normal, Poisson, and binomial distributions; linear regression models; classical estimation and model fitting methods; and frequentist methods of statistical inference. After forming this foundation, the authors explore multiple linear regression, analysis of variance (ANOVA), logistic regression, log-linear models, survival analysis, multilevel modeling, Bayesian models, and Markov chain Monte Carlo (MCMC) methods.

Using popular statistical software programs, this concise and accessible text illustrates practical approaches to estimation, model fitting, and model comparisons. It includes examples and exercises with complete data sets for nearly all the models covered.

This edition provides examples early in the text of practical problems such as the safety of a piece of engineering equipment or the inevitability of wrong conclusions in seemingly accurate medical tests for AIDS and cancer.;College or university bookstores may order five or more copies at a special student price which is available upon request from Marcel Dekker, Inc.

The main features also include:

• A brief introduction of the material in each chapter, followed by an explanation of the concepts presented.
• Examples and applications in each chapter that will help you cement your knowledge on the topics, encouraging you to participate in the problem-solving process.
• Highlighted key points and important results, helping you remember what you study – especially during the revision process.

The 5th Edition uses examples and exercises with a variety of “real data.” It includes more illustrations of statistical software for computations and takes advantage of the outstanding applets to explain key concepts, such as sampling distributions and conducting basic data analyses. It continues to downplay mathematics–often a stumbling block for students–while avoiding reliance on an overly simplistic recipe-based approach to statistics.